Energy efficiency in wireless sensor networks

Hi, my project is about energy efficiency in wireless sensor networks I WANT TO WORK ON GRAPH FORMATION to extend the coverage area and energy efficiency in wireless sensor networks. I need the MATLAB code for the optimization. Thank you

Answer 1

[said ouhmi]

hi Amith

Thank you for your effort and for providing this example. It’s exactly the direction I was looking for, and it will be a great starting point for my work on optimizing coverage and energy efficiency in wireless sensor networks. I appreciate the detailed explanation, especially the use of PSO for optimization. I will explore this approach and consider other alternatives as well.

Thanks again for your support.

Best regards,

Said Ouhmi

Answer 2

[Mathy]

Hi Said,

I understand that you are interested in learning about graph formation for optimization to increase coverage area while maintaining energy efficiency.

I can provide an example to help you get started. Below is an example that generates a random wireless sensor network, creates edges based on the maximum communication range, and plots the resulting graph. For the optimization function, I have used a Particle Swarm Optimization (PSO) algorithm to optimize the placement of nodes, minimizing the total communication distance and indirectly improving energy efficiency. Feel free to use an alternative optimization.

% Parameters
numNodes = 50; % Number of nodes
areaSize = 100; % Size of the area (100x100)
maxRange = 20; % Maximum communication range
numParticles = 30; % Number of particles in PSO
maxIterations = 100; % Maximum number of iterations in PSO

% Generate random node positions
nodePositions = rand(numNodes, 2) * areaSize;

% Initialize adjacency matrix
adjMatrix = zeros(numNodes);

% Create edges based on communication range
for i = 1:numNodes
    for j = i+1:numNodes
        distance = norm(nodePositions(i,:) - nodePositions(j,:));
        if distance <= maxRange
            adjMatrix(i,j) = 1;
            adjMatrix(j,i) = 1;
        end
    end
end

% PSO parameters
w = 0.5; % Inertia weight
c1 = 1.5; % Cognitive (personal) weight
c2 = 1.5; % Social (global) weight

% Initialize particles
particles = rand(numParticles, numNodes * 2) * areaSize;
velocities = zeros(numParticles, numNodes * 2);
personalBestPositions = particles;
personalBestScores = inf(numParticles, 1);
globalBestPosition = particles(1, :);
globalBestScore = inf;

% PSO optimization loop
for iter = 1:maxIterations
    for p = 1:numParticles
        % Update adjacency matrix for current particle
        currentPositions = reshape(particles(p, :), [numNodes, 2]);
        adjMatrix = zeros(numNodes);
        for i = 1:numNodes
            for j = i+1:numNodes
                distance = norm(currentPositions(i,:) - currentPositions(j,:));
                if distance <= maxRange
                    adjMatrix(i,j) = 1;
                    adjMatrix(j,i) = 1;
                end
            end
        end
        
        % Calculate fitness (total distance)
        totalDistance = optimizeGraph(adjMatrix, currentPositions);
        
        % Update personal best
        if totalDistance < personalBestScores(p)
            personalBestScores(p) = totalDistance;
            personalBestPositions(p, :) = particles(p, :);
        end
        
        % Update global best
        if totalDistance < globalBestScore
            globalBestScore = totalDistance;
            globalBestPosition = particles(p, :);
        end
    end
    
    % Update particle velocities and positions
    for p = 1:numParticles
        velocities(p, :) = w * velocities(p, :) ...
            + c1 * rand * (personalBestPositions(p, :) - particles(p, :)) ...
            + c2 * rand * (globalBestPosition - particles(p, :));
        particles(p, :) = particles(p, :) + velocities(p, :);
        
        % Ensure particles stay within bounds
        particles(p, :) = max(min(particles(p, :), areaSize), 0);
    end
end

% Display results
optimizedPositions = reshape(globalBestPosition, [numNodes, 2]);
adjMatrix = zeros(numNodes);
for i = 1:numNodes
    for j = i+1:numNodes
        distance = norm(optimizedPositions(i,:) - optimizedPositions(j,:));
        if distance <= maxRange
            adjMatrix(i,j) = 1;
            adjMatrix(j,i) = 1;
        end
    end
end

G = graph(adjMatrix);
figure;
plot(G, 'XData', optimizedPositions(:,1), 'YData', optimizedPositions(:,2));
title('Optimized Wireless Sensor Network Graph');
xlabel('X Position');
ylabel('Y Position');

disp(['Optimized Total Distance: ', num2str(globalBestScore)]);

% Optimization function (example: minimize total distance)
function totalDistance = optimizeGraph(adjMatrix, nodePositions)
    totalDistance = 0;
    [rows, cols] = find(adjMatrix);
    for k = 1:length(rows)
        totalDistance = totalDistance + norm(nodePositions(rows(k),:) - nodePositions(cols(k),:));
    end
end

The provided optimization code can serve as a placeholder for any custom or improved optimizations. The code generates a graph as illustrated below:

Please note that the points generated are random and the graph might vary accordingly.

I hope this helps!